Date: (Thu) Apr 21, 2016

Introduction:

Data: Source: Training:
https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/IBMStock.csv https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/GEStock.csv https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/ProcterGambleStock.csv https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/CocaColaStock.csv https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/BoeingStock.csv
New:
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = c(NULL
    ,IBM =  "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/IBMStock.csv"
    ,GE  =  "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/GEStock.csv"
    ,PG  =  "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/ProcterGambleStock.csv"
    ,CC  =  "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/CocaColaStock.csv"
    ,BG  =  "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/BoeingStock.csv"
                    ))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    # )                   
 

glbObsNewFile <- NULL # default OR list(url = "<obsNewFileName>") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- NULL # or TRUE or FALSE

glb_rsp_var_raw <- "StockPrice"

# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "StockPrice.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL 
# function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))    
#     }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
#print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany")) 

glb_map_rsp_var_to_raw <- NULL 
# function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
# }
#print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# Date: the date of the stock price, always given as the first of the month.
# StockPrice: the average stock price of the company in the given month.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- NULL # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- ".inp" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & work each one in
    , "Date.year.fctr"
                    ) 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

glb_assign_pairs_lst <- NULL; 
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
#                                            to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(.rnorm) { return(1:length(.rnorm)) }       
    , args = c(".rnorm"))    
glbFeatsDerive[[".pos.y"]] <- list(
    mapfn = function(.rnorm) { return(1:length(.rnorm)) }       
    , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

glb_derive_vars <- names(glbFeatsDerive)

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsDateTime[["Date"]] <- 
    c(format = "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
      last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")  # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsout_df) {
#     require(tidyr)
#     obsout_df <- obsout_df %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsout_df %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsout_df %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsout_df, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsout_df) {
#                   }
                  )
#obsout_df <- savobsout_df
# glbObsOut$mapFn <- function(obsout_df) {
#     txfout_df <- dplyr::select(obsout_df, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsout_df <- glbObsOut$mapFn(obsout_df); print(head(obsout_df))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    glbObsOut$vars[["Probability1"]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob]" 
#     glbObsOut$vars[[glb_rsp_var_raw]] <- 
#         "%<d-% glb_map_rsp_var_to_raw(glbObsNew[, 
#                                             mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value])"         
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Stocks_BlueChips_2016_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk 
#mysavChunk(glbOut$pfx, glbChunks[["last"]])

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("Stocks_BlueChips_2016_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, "import.data")
##         label step_major step_minor label_minor   bgn end elapsed
## 1 import.data          1          0           0 6.273  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/IBMStock.csv..."
## [1] "dimensions of data in ./data/IBMStock.csv: 480 rows x 2 cols"
## [1] "Reading file ./data/GEStock.csv..."
## [1] "dimensions of data in ./data/GEStock.csv: 480 rows x 2 cols"
## [1] "Reading file ./data/ProcterGambleStock.csv..."
## [1] "dimensions of data in ./data/ProcterGambleStock.csv: 480 rows x 2 cols"
## [1] "Reading file ./data/CocaColaStock.csv..."
## [1] "dimensions of data in ./data/CocaColaStock.csv: 480 rows x 2 cols"
## [1] "Reading file ./data/BoeingStock.csv..."
## [1] "dimensions of data in ./data/BoeingStock.csv: 480 rows x 2 cols"
##     Date StockPrice .inp
## 1 1/1/70   360.3190  IBM
## 2 2/1/70   346.7237  IBM
## 3 3/1/70   327.3457  IBM
## 4 4/1/70   319.8527  IBM
## 5 5/1/70   270.3752  IBM
## 6 6/1/70   267.2050  IBM
##         Date StockPrice .inp
## 83   11/1/76  267.15050  IBM
## 366   6/1/00  114.26818  IBM
## 1223 11/1/91   82.37900   PG
## 1746  6/1/95   62.29045   CC
## 1762 10/1/96   49.75217   CC
## 2374 10/1/07   98.90652   BG
##         Date StockPrice .inp
## 2395  7/1/09   41.48273   BG
## 2396  8/1/09   45.99429   BG
## 2397  9/1/09   51.36286   BG
## 2398 10/1/09   51.15909   BG
## 2399 11/1/09   50.69650   BG
## 2400 12/1/09   55.02864   BG
## 'data.frame':    2400 obs. of  3 variables:
##  $ Date      : chr  "1/1/70" "2/1/70" "3/1/70" "4/1/70" ...
##  $ StockPrice: num  360 347 327 320 270 ...
##  $ .inp      : chr  "IBM" "IBM" "IBM" "IBM" ...
##  - attr(*, "comment")= chr "glbObsTrn"
## NULL
## Warning: No file specified for glbObsNew & splitSpecs$method not specified.
## Defaulting to copy.
##     Date StockPrice .inp
## 1 1/1/70   360.3190  IBM
## 2 2/1/70   346.7237  IBM
## 3 3/1/70   327.3457  IBM
## 4 4/1/70   319.8527  IBM
## 5 5/1/70   270.3752  IBM
## 6 6/1/70   267.2050  IBM
##        Date StockPrice .inp
## 3    3/1/70  327.34571  IBM
## 429  9/1/05   79.58857  IBM
## 931  7/1/07   39.39810   GE
## 939  3/1/08   34.80150   GE
## 965  5/1/70   73.33286   PG
## 1110 6/1/82   83.65682   PG
##         Date StockPrice .inp
## 2395  7/1/09   41.48273   BG
## 2396  8/1/09   45.99429   BG
## 2397  9/1/09   51.36286   BG
## 2398 10/1/09   51.15909   BG
## 2399 11/1/09   50.69650   BG
## 2400 12/1/09   55.02864   BG
## 'data.frame':    2400 obs. of  3 variables:
##  $ Date      : chr  "1/1/70" "2/1/70" "3/1/70" "4/1/70" ...
##  $ StockPrice: num  360 347 327 320 270 ...
##  $ .inp      : chr  "IBM" "IBM" "IBM" "IBM" ...
##  - attr(*, "comment")= chr "glbObsNew"
##     Date StockPrice .inp
## 1 1/1/70   360.3190  IBM
## 2 2/1/70   346.7237  IBM
## 3 3/1/70   327.3457  IBM
## 4 4/1/70   319.8527  IBM
## 5 5/1/70   270.3752  IBM
## 6 6/1/70   267.2050  IBM
##         Date StockPrice .inp
## 784   4/1/95   54.98947   GE
## 1089  9/1/80   76.83714   PG
## 1544  8/1/78   45.08957   CC
## 1696  4/1/91   54.12682   CC
## 2284  4/1/00   37.15211   BG
## 2315 11/1/02   32.19400   BG
##         Date StockPrice .inp
## 2395  7/1/09   41.48273   BG
## 2396  8/1/09   45.99429   BG
## 2397  9/1/09   51.36286   BG
## 2398 10/1/09   51.15909   BG
## 2399 11/1/09   50.69650   BG
## 2400 12/1/09   55.02864   BG
## 'data.frame':    2400 obs. of  3 variables:
##  $ Date      : chr  "1/1/70" "2/1/70" "3/1/70" "4/1/70" ...
##  $ StockPrice: num  360 347 327 320 270 ...
##  $ .inp      : chr  "IBM" "IBM" "IBM" "IBM" ...
##  - attr(*, "comment")= chr "glbObsTrn"
## Warning: glbObsTrn same as glbObsAll
## Warning: glbObsNew same as glbObsAll
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: .pos.y..."
## Warning: using .rownames as identifiers for observations
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##   StockPrice.cut.fctr  .src   .n
## 1          (8.86,152]  Test 2269
## 2          (8.86,152] Train 2269
## 3           (152,296]  Test   88
## 4           (152,296] Train   88
## 5           (296,439]  Test   43
## 6           (296,439] Train   43
##   StockPrice.cut.fctr  .src   .n
## 1          (8.86,152]  Test 2269
## 2          (8.86,152] Train 2269
## 3           (152,296]  Test   88
## 4           (152,296] Train   88
## 5           (296,439]  Test   43
## 6           (296,439] Train   43
## Loading required package: RColorBrewer

##    .src   .n
## 1  Test 2400
## 2 Train 2400
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor   bgn   end elapsed
## 1  import.data          1          0           0 6.273 8.854   2.581
## 2 inspect.data          2          0           0 8.854    NA      NA

Step 2.0: inspect data

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Date .inp 
##    0    0

## NULL
##          label step_major step_minor label_minor    bgn   end elapsed
## 2 inspect.data          2          0           0  8.854 48.69  39.836
## 3   scrub.data          2          1           1 48.691    NA      NA

Step 2.1: scrub data

## [1] "numeric data missing in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Date .inp 
##    0    0
##            label step_major step_minor label_minor    bgn    end elapsed
## 3     scrub.data          2          1           1 48.691 76.225  27.534
## 4 transform.data          2          2           2 76.226     NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor    bgn    end elapsed
## 4   transform.data          2          2           2 76.226 76.267   0.041
## 5 extract.features          3          0           0 76.267     NA      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor    bgn
## 5          extract.features          3          0           0 76.267
## 6 extract.features.datetime          3          1           1 76.288
##      end elapsed
## 5 76.288   0.021
## 6     NA      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor    bgn
## 1 extract.features.datetime.bgn          1          0           0 76.315
##   end elapsed
## 1  NA      NA
##                                   label step_major step_minor label_minor
## 1         extract.features.datetime.bgn          1          0           0
## 2 extract.features_xtract.DateTime.vars          2          0           0
##      bgn    end elapsed
## 1 76.315 76.322   0.008
## 2 76.323     NA      NA
## [1] "Extracting features from DateTime(s): Date"

## Loading required package: XML
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1970-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1970-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1971-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1971-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1972-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1972-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1973-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1973-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1974-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1974-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1975-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1975-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1976-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1976-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1977-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1977-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1978-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1978-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1979-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1979-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1980-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1980-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1981-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1981-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1982-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1982-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1983-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1983-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1984-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1984-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1985-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1985-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1986-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1986-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1987-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1987-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1988-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1988-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1989-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1989-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1990-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1990-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1991-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1991-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1992-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1992-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1993-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1993-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1994-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1994-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1995-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1995-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1996-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1996-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1997-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1997-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1998-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1998-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/1999-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/1999-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2000-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2000-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2001-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2001-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2002-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2002-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2003-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2003-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2004-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2004-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2005-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2005-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2006-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2006-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2007-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2007-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2008-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2008-federal-holidays.htm; skipping ...
## [1] "   accessing url: http://about.usps.com/news/events-calendar/2009-federal-holidays.htm"
## Warning in myextract_dates_df(df = glbObsAll, vars =
## names(glbFeatsDateTime), : unable to access url:http://about.usps.com/news/
## events-calendar/2009-federal-holidays.htm; skipping ...

## [1] "**********"
## [1] "Consider adding state & city holidays for glbFeatsDateTime: Date"
## [1] "**********"

## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## [1] "Missing data for numerics:"
##  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p Date.last16.log1p 
##                 2                 4                 8                16 
## Date.last32.log1p 
##                32
## Loading required package: mice
## Loading required package: Rcpp
## mice 2.25 2015-11-09

## [1] "Summary before imputation: "
##      Date               .inp               .src          
##  Length:4800        Length:4800        Length:4800       
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##                                                          
##      .rnorm              .pos          .pos.y       .rownames   
##  Min.   :-3.89398   Min.   :   1   Min.   :   1   Min.   :   1  
##  1st Qu.:-0.68672   1st Qu.:1201   1st Qu.:1201   1st Qu.:1201  
##  Median : 0.01802   Median :2400   Median :2400   Median :2400  
##  Mean   : 0.00897   Mean   :2400   Mean   :2400   Mean   :2400  
##  3rd Qu.: 0.69953   3rd Qu.:3600   3rd Qu.:3600   3rd Qu.:3600  
##  Max.   : 3.74468   Max.   :4800   Max.   :4800   Max.   :4800  
##                                                                 
##  Date.year.fctr Date.month.fctr         Date.date.fctr Date.juliandate
##  1970   : 120   01     : 400    (0.999,0.9994] :   0   Min.   :  1.0  
##  1971   : 120   02     : 400    (0.9994,0.9998]:   0   1st Qu.: 83.5  
##  1972   : 120   03     : 400    (0.9998,1.0002]:4800   Median :167.5  
##  1973   : 120   04     : 400    (1.0002,1.0006]:   0   Mean   :167.7  
##  1974   : 120   05     : 400    (1.0006,1.001] :   0   3rd Qu.:252.2  
##  1975   : 120   06     : 400                           Max.   :336.0  
##  (Other):4080   (Other):2400                                          
##  Date.wkday.fctr   Date.wkend       Date.hlday Date.hour.fctr
##  0:690           Min.   :0.0000   Min.   :0    Min.   :0     
##  1:680           1st Qu.:0.0000   1st Qu.:0    1st Qu.:0     
##  2:690           Median :0.0000   Median :0    Median :0     
##  3:680           Mean   :0.2875   Mean   :0    Mean   :0     
##  4:700           3rd Qu.:1.0000   3rd Qu.:0    3rd Qu.:0     
##  5:670           Max.   :1.0000   Max.   :0    Max.   :0     
##  6:690                                                       
##  Date.minute.fctr Date.second.fctr Date.day.minutes     .order    
##  Min.   :0        Min.   :0        Min.   :0        Min.   :   1  
##  1st Qu.:0        1st Qu.:0        1st Qu.:0        1st Qu.:1201  
##  Median :0        Median :0        Median :0        Median :2400  
##  Mean   :0        Mean   :0        Mean   :0        Mean   :2400  
##  3rd Qu.:0        3rd Qu.:0        3rd Qu.:0        3rd Qu.:3600  
##  Max.   :0        Max.   :0        Max.   :0        Max.   :4800  
##                                                                   
##  Date.last2.log1p Date.last4.log1p Date.last8.log1p Date.last16.log1p
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.00    Min.   :14.70    
##  1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:14.70    1st Qu.:14.80    
##  Median : 0.000   Median : 0.000   Median :14.77    Median :15.46    
##  Mean   : 2.951   Mean   : 5.905   Mean   :11.82    Mean   :15.20    
##  3rd Qu.: 0.000   3rd Qu.:14.768   3rd Qu.:14.80    3rd Qu.:15.48    
##  Max.   :14.802   Max.   :14.802   Max.   :14.80    Max.   :15.49    
##  NA's   :2        NA's   :4        NA's   :8        NA's   :16       
##  Date.last32.log1p
##  Min.   :15.85    
##  1st Qu.:15.88    
##  Median :15.89    
##  Mean   :15.94    
##  3rd Qu.:15.89    
##  Max.   :16.18    
##  NA's   :32       
## 
##  iter imp variable
##   1   1  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   1   2  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   1   3  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   1   4  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   1   5  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   2   1  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   2   2  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   2   3  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   2   4  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   2   5  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   3   1  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   3   2  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   3   3  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   3   4  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   3   5  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   4   1  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   4   2  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   4   3  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   4   4  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   4   5  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   5   1  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   5   2  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   5   3  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   5   4  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##   5   5  Date.last2.log1p  Date.last4.log1p  Date.last8.log1p  Date.last16.log1p  Date.last32.log1p
##      .rnorm              .pos          .pos.y       .rownames   
##  Min.   :-3.89398   Min.   :   1   Min.   :   1   Min.   :   1  
##  1st Qu.:-0.68672   1st Qu.:1201   1st Qu.:1201   1st Qu.:1201  
##  Median : 0.01802   Median :2400   Median :2400   Median :2400  
##  Mean   : 0.00897   Mean   :2400   Mean   :2400   Mean   :2400  
##  3rd Qu.: 0.69953   3rd Qu.:3600   3rd Qu.:3600   3rd Qu.:3600  
##  Max.   : 3.74468   Max.   :4800   Max.   :4800   Max.   :4800  
##                                                                 
##  Date.year.fctr Date.month.fctr         Date.date.fctr Date.juliandate
##  1970   : 120   01     : 400    (0.999,0.9994] :   0   Min.   :  1.0  
##  1971   : 120   02     : 400    (0.9994,0.9998]:   0   1st Qu.: 83.5  
##  1972   : 120   03     : 400    (0.9998,1.0002]:4800   Median :167.5  
##  1973   : 120   04     : 400    (1.0002,1.0006]:   0   Mean   :167.7  
##  1974   : 120   05     : 400    (1.0006,1.001] :   0   3rd Qu.:252.2  
##  1975   : 120   06     : 400                           Max.   :336.0  
##  (Other):4080   (Other):2400                                          
##  Date.wkday.fctr   Date.wkend       Date.hlday Date.hour.fctr
##  0:690           Min.   :0.0000   Min.   :0    Min.   :0     
##  1:680           1st Qu.:0.0000   1st Qu.:0    1st Qu.:0     
##  2:690           Median :0.0000   Median :0    Median :0     
##  3:680           Mean   :0.2875   Mean   :0    Mean   :0     
##  4:700           3rd Qu.:1.0000   3rd Qu.:0    3rd Qu.:0     
##  5:670           Max.   :1.0000   Max.   :0    Max.   :0     
##  6:690                                                       
##  Date.minute.fctr Date.second.fctr Date.day.minutes     .order    
##  Min.   :0        Min.   :0        Min.   :0        Min.   :   1  
##  1st Qu.:0        1st Qu.:0        1st Qu.:0        1st Qu.:1201  
##  Median :0        Median :0        Median :0        Median :2400  
##  Mean   :0        Mean   :0        Mean   :0        Mean   :2400  
##  3rd Qu.:0        3rd Qu.:0        3rd Qu.:0        3rd Qu.:3600  
##  Max.   :0        Max.   :0        Max.   :0        Max.   :4800  
##                                                                   
##  Date.last2.log1p Date.last4.log1p Date.last8.log1p Date.last16.log1p
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.00    Min.   :14.70    
##  1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:14.70    1st Qu.:14.80    
##  Median : 0.000   Median : 0.000   Median :14.77    Median :15.46    
##  Mean   : 2.956   Mean   : 5.913   Mean   :11.83    Mean   :15.20    
##  3rd Qu.: 0.000   3rd Qu.:14.768   3rd Qu.:14.80    3rd Qu.:15.48    
##  Max.   :14.802   Max.   :14.802   Max.   :14.80    Max.   :15.49    
##                                                                      
##  Date.last32.log1p
##  Min.   :15.85    
##  1st Qu.:15.88    
##  Median :15.89    
##  Mean   :15.94    
##  3rd Qu.:15.89    
##  Max.   :16.18    
## 

##                       label step_major step_minor label_minor     bgn
## 6 extract.features.datetime          3          1           1  76.288
## 7    extract.features.image          3          2           2 110.590
##      end elapsed
## 6 110.59  34.302
## 7     NA      NA

Step 3.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.image.bgn          1          0           0 114.074  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 114.074
## 2 extract.features.image.end          2          0           0 114.084
##       end elapsed
## 1 114.083    0.01
## 2      NA      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 114.074
## 2 extract.features.image.end          2          0           0 114.084
##       end elapsed
## 1 114.083    0.01
## 2      NA      NA
##                    label step_major step_minor label_minor     bgn     end
## 7 extract.features.image          3          2           2 110.590 114.094
## 8 extract.features.price          3          3           3 114.094      NA
##   elapsed
## 7   3.504
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.price.bgn          1          0           0 114.122  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor     bgn     end
## 8 extract.features.price          3          3           3 114.094 114.132
## 9  extract.features.text          3          4           4 114.133      NA
##   elapsed
## 8   0.039
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor     bgn end
## 1 extract.features.text.bgn          1          0           0 114.177  NA
##   elapsed
## 1      NA
##                      label step_major step_minor label_minor     bgn
## 9    extract.features.text          3          4           4 114.133
## 10 extract.features.string          3          5           5 114.187
##        end elapsed
## 9  114.186   0.053
## 10      NA      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor     bgn
## 1 extract.features.string.bgn          1          0           0 114.219
##   end elapsed
## 1  NA      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor     bgn     end elapsed
## 1           0 114.219 114.229   0.011
## 2           0 114.230      NA      NA
##   Date   .inp   .src 
## "Date" ".inp" ".src"
## Warning: Creating factors of string variable: .inp: # of unique values: 5
##                      label step_major step_minor label_minor     bgn
## 10 extract.features.string          3          5           5 114.187
## 11    extract.features.end          3          6           6 114.247
##        end elapsed
## 10 114.247    0.06
## 11      NA      NA

Step 3.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor     bgn    end
## 11 extract.features.end          3          6           6 114.247 115.16
## 12  manage.missing.data          4          0           0 115.160     NA
##    elapsed
## 11   0.913
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
##  Date.wkday.fctr       Date.wkend       Date.hlday   Date.hour.fctr 
##              690             3420             4800             4800 
## Date.minute.fctr Date.second.fctr Date.day.minutes Date.last2.log1p 
##             4800             4800             4800             3840 
## Date.last4.log1p Date.last8.log1p 
##             2880              960 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Date .inp 
##    0    0
## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
##  Date.wkday.fctr       Date.wkend       Date.hlday   Date.hour.fctr 
##              690             3420             4800             4800 
## Date.minute.fctr Date.second.fctr Date.day.minutes Date.last2.log1p 
##             4800             4800             4800             3840 
## Date.last4.log1p Date.last8.log1p 
##             2880              960 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Date .inp 
##    0    0
##                  label step_major step_minor label_minor     bgn     end
## 12 manage.missing.data          4          0           0 115.160 115.476
## 13        cluster.data          5          0           0 115.476      NA
##    elapsed
## 12   0.316
## 13      NA

Step 5.0: cluster data

##                      label step_major step_minor label_minor     bgn
## 13            cluster.data          5          0           0 115.476
## 14 partition.data.training          6          0           0 115.529
##        end elapsed
## 13 115.529   0.053
## 14      NA      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "Newdata contains non-NA data for StockPrice; setting OOB to Newdata"
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 0.01 secs"
##   .inp .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## 1   BG    480    480    480            0.2            0.2            0.2
## 2   CC    480    480    480            0.2            0.2            0.2
## 3   GE    480    480    480            0.2            0.2            0.2
## 4  IBM    480    480    480            0.2            0.2            0.2
## 5   PG    480    480    480            0.2            0.2            0.2
## [1] "glbObsAll: "
## [1] 4800   28
## [1] "glbObsTrn: "
## [1] 2400   28
## [1] "glbObsFit: "
## [1] 2400   27
## [1] "glbObsOOB: "
## [1] 2400   27
## [1] "glbObsNew: "
## [1] 2400   27
## [1] "partition.data.training chunk: teardown: elapsed: 0.22 secs"
##                      label step_major step_minor label_minor     bgn
## 14 partition.data.training          6          0           0 115.529
## 15         select.features          7          0           0 115.812
##        end elapsed
## 14 115.811   0.282
## 15      NA      NA

Step 7.0: select features

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
## Loading required package: reshape2
## [1] "cor(.pos, .pos.y)=1.0000"
## [1] "cor(StockPrice, .pos)=-0.5304"
## [1] "cor(StockPrice, .pos.y)=-0.5304"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified .pos.y as highly correlated with .pos
## [1] "cor(.pos, .rownames)=1.0000"
## [1] "cor(StockPrice, .pos)=-0.5304"
## [1] "cor(StockPrice, .rownames)=-0.5304"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified .rownames as highly correlated with .pos
## [1] "cor(Date.juliandate, Date.month.fctr)=1.0000"
## [1] "cor(StockPrice, Date.juliandate)=-0.0337"
## [1] "cor(StockPrice, Date.month.fctr)=-0.0337"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Date.juliandate as highly correlated with
## Date.month.fctr
## [1] "cor(Date.last2.log1p, Date.last32.log1p)=0.9976"
## [1] "cor(StockPrice, Date.last2.log1p)=0.3532"
## [1] "cor(StockPrice, Date.last32.log1p)=0.3511"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Date.last32.log1p as highly correlated with
## Date.last2.log1p
## [1] "cor(.pos, Date.last2.log1p)=-0.8485"
## [1] "cor(StockPrice, .pos)=-0.5304"
## [1] "cor(StockPrice, Date.last2.log1p)=0.3532"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Date.last2.log1p as highly correlated with .pos
## [1] "cor(.inp.fctr, Date.last4.log1p)=0.7071"
## [1] "cor(StockPrice, .inp.fctr)=0.3700"
## [1] "cor(StockPrice, Date.last4.log1p)=0.2767"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glbObsTrn, : Identified Date.last4.log1p as highly correlated
## with .inp.fctr
##                           cor.y exclude.as.feat    cor.y.abs
## .inp.fctr          3.699980e-01               0 3.699980e-01
## Date.last2.log1p   3.532134e-01               0 3.532134e-01
## Date.last32.log1p  3.510897e-01               0 3.510897e-01
## Date.last4.log1p   2.767099e-01               0 2.767099e-01
## Date.wkday.fctr    6.166083e-03               0 6.166083e-03
## Date.wkend        -6.905542e-05               0 6.905542e-05
## .rnorm            -1.152039e-03               0 1.152039e-03
## Date.last8.log1p  -1.182108e-02               0 1.182108e-02
## Date.last16.log1p -3.163526e-02               0 3.163526e-02
## Date.juliandate   -3.365921e-02               0 3.365921e-02
## Date.month.fctr   -3.367924e-02               0 3.367924e-02
## Date.year.fctr    -2.415176e-01               1 2.415176e-01
## Date.POSIX        -2.422818e-01               1 2.422818e-01
## Date.zoo          -5.303143e-01               1 5.303143e-01
## .pos              -5.303686e-01               0 5.303686e-01
## .pos.y            -5.303686e-01               0 5.303686e-01
## .rownames         -5.303686e-01               0 5.303686e-01
## Date.date.fctr               NA               0           NA
## Date.day.minutes             NA               1           NA
## Date.hlday                   NA               0           NA
## Date.hour.fctr               NA               0           NA
## Date.minute.fctr             NA               0           NA
## Date.second.fctr             NA               0           NA
##                         cor.high.X freqRatio percentUnique zeroVar   nzv
## .inp.fctr                     <NA>  1.000000    0.20833333   FALSE FALSE
## Date.last2.log1p              .pos  3.025210    0.41666667   FALSE FALSE
## Date.last32.log1p Date.last2.log1p  2.046610    0.87500000   FALSE FALSE
## Date.last4.log1p         .inp.fctr  1.959016    0.41666667   FALSE FALSE
## Date.wkday.fctr               <NA>  1.014493    0.29166667   FALSE FALSE
## Date.wkend                    <NA>  2.478261    0.08333333   FALSE FALSE
## .rnorm                        <NA>  1.000000  100.00000000   FALSE FALSE
## Date.last8.log1p              <NA>  1.949264    0.37500000   FALSE FALSE
## Date.last16.log1p             <NA>  2.116456    0.37500000   FALSE FALSE
## Date.juliandate    Date.month.fctr  1.000000    0.91666667   FALSE FALSE
## Date.month.fctr               <NA>  1.000000    0.50000000   FALSE FALSE
## Date.year.fctr                <NA>  1.000000    1.66666667   FALSE FALSE
## Date.POSIX                    <NA>  1.000000   20.00000000   FALSE FALSE
## Date.zoo                      <NA>  1.000000   10.00000000   FALSE FALSE
## .pos                          <NA>  1.000000  100.00000000   FALSE FALSE
## .pos.y                        .pos  1.000000  100.00000000   FALSE FALSE
## .rownames                     .pos  1.000000  100.00000000   FALSE FALSE
## Date.date.fctr                <NA>  0.000000    0.04166667    TRUE  TRUE
## Date.day.minutes              <NA>  0.000000    0.04166667    TRUE  TRUE
## Date.hlday                    <NA>  0.000000    0.04166667    TRUE  TRUE
## Date.hour.fctr                <NA>  0.000000    0.04166667    TRUE  TRUE
## Date.minute.fctr              <NA>  0.000000    0.04166667    TRUE  TRUE
## Date.second.fctr              <NA>  0.000000    0.04166667    TRUE  TRUE
##                   is.cor.y.abs.low
## .inp.fctr                    FALSE
## Date.last2.log1p             FALSE
## Date.last32.log1p            FALSE
## Date.last4.log1p             FALSE
## Date.wkday.fctr              FALSE
## Date.wkend                    TRUE
## .rnorm                       FALSE
## Date.last8.log1p             FALSE
## Date.last16.log1p            FALSE
## Date.juliandate              FALSE
## Date.month.fctr              FALSE
## Date.year.fctr               FALSE
## Date.POSIX                   FALSE
## Date.zoo                     FALSE
## .pos                         FALSE
## .pos.y                       FALSE
## .rownames                    FALSE
## Date.date.fctr                  NA
## Date.day.minutes                NA
## Date.hlday                      NA
## Date.hour.fctr                  NA
## Date.minute.fctr                NA
## Date.second.fctr                NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_point).

##                  cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Date.date.fctr      NA               0        NA       <NA>         0
## Date.day.minutes    NA               1        NA       <NA>         0
## Date.hlday          NA               0        NA       <NA>         0
## Date.hour.fctr      NA               0        NA       <NA>         0
## Date.minute.fctr    NA               0        NA       <NA>         0
## Date.second.fctr    NA               0        NA       <NA>         0
##                  percentUnique zeroVar  nzv is.cor.y.abs.low
## Date.date.fctr      0.04166667    TRUE TRUE               NA
## Date.day.minutes    0.04166667    TRUE TRUE               NA
## Date.hlday          0.04166667    TRUE TRUE               NA
## Date.hour.fctr      0.04166667    TRUE TRUE               NA
## Date.minute.fctr    0.04166667    TRUE TRUE               NA
## Date.second.fctr    0.04166667    TRUE TRUE               NA

## [1] "numeric data missing in : "
## named integer(0)
## [1] "numeric data w/ 0s in : "
##  Date.wkday.fctr       Date.wkend       Date.hlday   Date.hour.fctr 
##              690             3420             4800             4800 
## Date.minute.fctr Date.second.fctr Date.day.minutes Date.last2.log1p 
##             4800             4800             4800             3840 
## Date.last4.log1p Date.last8.log1p 
##             2880              960 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Date .inp .lcn 
##    0    0    0
## [1] "glb_feats_df:"
## [1] 23 12
##                    id exclude.as.feat rsp_var
## StockPrice StockPrice            TRUE    TRUE
##                    id cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## StockPrice StockPrice    NA            TRUE        NA       <NA>        NA
##            percentUnique zeroVar nzv is.cor.y.abs.low interaction.feat
## StockPrice            NA      NA  NA               NA               NA
##            shapiro.test.p.value rsp_var_raw rsp_var
## StockPrice                   NA          NA    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn     end
## 15 select.features          7          0           0 115.812 117.848
## 16      fit.models          8          0           0 117.848      NA
##    elapsed
## 15   2.036
## 16      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 118.407  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor label_minor     bgn
## 1               fit.models_0_bgn          1          0       setup 118.407
## 2 fit.models_0_Max.cor.Y.rcv.*X*          1          1      glmnet 118.444
##       end elapsed
## 1 118.444   0.037
## 2      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: .pos,.inp.fctr"
## [1] "myfit_mdl: setup complete: 1.007000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.668 on full training set
## [1] "myfit_mdl: train complete: 1.670000 secs"

##             Length Class      Mode     
## a0          100    -none-     numeric  
## beta        500    dgCMatrix  S4       
## df          100    -none-     numeric  
## dim           2    -none-     numeric  
## lambda      100    -none-     numeric  
## dev.ratio   100    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        5    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     1    -none-     logical  
## [1] "min lambda > lambdaOpt:"
##  (Intercept)  .inp.fctrCC  .inp.fctrGE .inp.fctrIBM  .inp.fctrPG 
##  168.3066609  -12.0627130  -67.0756292   -9.9379418  -21.7091644 
##         .pos 
##   -0.0570999 
## [1] "max lambda < lambdaOpt:"
##  (Intercept)  .inp.fctrCC  .inp.fctrGE .inp.fctrIBM  .inp.fctrPG 
## 172.44724646 -13.04655359 -69.90089659 -13.61490564 -23.59904481 
##         .pos 
##  -0.05898694 
## [1] "myfit_mdl: train diagnostics complete: 1.742000 secs"
## [1] "myfit_mdl: predict complete: 1.900000 secs"
##                           id          feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet .pos,.inp.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1                      0.656                 0.011    0.4354461
##   min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1      42.0929         0.434267    -5.871796     146.8559        -5.886148
## [1] "myfit_mdl: exit: 1.905000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: .pos,.inp.fctr"
## [1] "myfit_mdl: setup complete: 0.708000 secs"
## Loading required package: rpart
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0177 on full training set
## [1] "myfit_mdl: train complete: 2.360000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 2400 
## 
##           CP nsplit rel error
## 1 0.68121025      0 1.0000000
## 2 0.06636897      1 0.3187897
## 3 0.03265173      2 0.2524208
## 4 0.03109733      3 0.2197691
## 5 0.01766860      4 0.1886717
## 
## Variable importance
##         .pos .inp.fctrIBM  .inp.fctrPG  .inp.fctrCC  .inp.fctrGE 
##           89            7            1            1            1 
## 
## Node number 1: 2400 observations,    complexity param=0.6812103
##   mean=77.60114, MSE=3138.429 
##   left son=2 (2287 obs) right son=3 (113 obs)
##   Primary splits:
##       .pos         < 113.5  to the right, improve=6.812103e-01, (0 missing)
##       .inp.fctrIBM < 0.5    to the left,  improve=3.551739e-01, (0 missing)
##       .inp.fctrGE  < 0.5    to the right, improve=2.666968e-02, (0 missing)
##       .inp.fctrCC  < 0.5    to the right, improve=2.459468e-02, (0 missing)
##       .inp.fctrPG  < 0.5    to the left,  improve=8.512303e-07, (0 missing)
## 
## Node number 2: 2287 observations,    complexity param=0.06636897
##   mean=67.32328, MSE=852.3751 
##   left son=4 (1903 obs) right son=5 (384 obs)
##   Primary splits:
##       .pos         < 497.5  to the right, improve=0.25644350, (0 missing)
##       .inp.fctrIBM < 0.5    to the left,  improve=0.25262720, (0 missing)
##       .inp.fctrPG  < 0.5    to the left,  improve=0.03358540, (0 missing)
##       .inp.fctrGE  < 0.5    to the right, improve=0.02004364, (0 missing)
##       .inp.fctrCC  < 0.5    to the right, improve=0.01657792, (0 missing)
##   Surrogate splits:
##       .inp.fctrIBM < 0.5    to the left,  agree=0.993, adj=0.956, (0 split)
## 
## Node number 3: 113 observations,    complexity param=0.03265173
##   mean=285.6142, MSE=3998.366 
##   left son=6 (69 obs) right son=7 (44 obs)
##   Primary splits:
##       .pos < 44.5   to the right, improve=0.544338, (0 missing)
## 
## Node number 4: 1903 observations,    complexity param=0.03109733
##   mean=60.68191, MSE=602.5245 
##   left son=8 (871 obs) right son=9 (1032 obs)
##   Primary splits:
##       .pos        < 1529.5 to the right, improve=0.2042834000, (0 missing)
##       .inp.fctrPG < 0.5    to the left,  improve=0.1622234000, (0 missing)
##       .inp.fctrGE < 0.5    to the right, improve=0.0029852640, (0 missing)
##       .inp.fctrCC < 0.5    to the right, improve=0.0002381228, (0 missing)
##   Surrogate splits:
##       .inp.fctrPG < 0.5    to the left,  agree=0.710, adj=0.366, (0 split)
##       .inp.fctrCC < 0.5    to the right, agree=0.701, adj=0.347, (0 split)
##       .inp.fctrGE < 0.5    to the left,  agree=0.701, adj=0.347, (0 split)
## 
## Node number 5: 384 observations
##   mean=100.2361, MSE=788.7282 
## 
## Node number 6: 69 observations
##   mean=248.3598, MSE=1345.133 
## 
## Node number 7: 44 observations
##   mean=344.0358, MSE=2569.565 
## 
## Node number 8: 871 observations
##   mean=48.60559, MSE=304.1865 
## 
## Node number 9: 1032 observations
##   mean=70.87423, MSE=627.3502 
## 
## n= 2400 
## 
## node), split, n, deviance, yval
##       * denotes terminal node
## 
## 1) root 2400 7532229.0  77.60114  
##   2) .pos>=113.5 2287 1949382.0  67.32328  
##     4) .pos>=497.5 1903 1146604.0  60.68191  
##       8) .pos>=1529.5 871  264946.5  48.60559 *
##       9) .pos< 1529.5 1032  647425.4  70.87423 *
##     5) .pos< 497.5 384  302871.6 100.23610 *
##   3) .pos< 113.5 113  451815.3 285.61420  
##     6) .pos>=44.5 69   92814.2 248.35980 *
##     7) .pos< 44.5 44  113060.9 344.03580 *
## [1] "myfit_mdl: train diagnostics complete: 3.089000 secs"
## [1] "myfit_mdl: predict complete: 3.138000 secs"
##                     id          feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart .pos,.inp.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1                      1.646                  0.02    0.8113283
##   min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1      24.2999               NA   -0.2678863     63.08067               NA
##   max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1        0.8083695         1.5931         0.03917691
## [1] "myfit_mdl: exit: 3.148000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##                             label step_major step_minor label_minor
## 2  fit.models_0_Max.cor.Y.rcv.*X*          1          1      glmnet
## 3 fit.models_0_Max.cor.Y.Time.Lag          1          2      glmnet
##       bgn     end elapsed
## 2 118.444 123.609   5.165
## 3 123.610      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.Time.Lag##rcv#glmnet"
## [1] "    indepVar: .pos,.inp.fctr,Date.last2.log1p,Date.last4.log1p,Date.last8.log1p,Date.last16.log1p,Date.last32.log1p"
## [1] "myfit_mdl: setup complete: 0.716000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.031 on full training set
## [1] "myfit_mdl: train complete: 3.087000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y.Time.Lag", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           82    -none-     numeric  
## beta        820    dgCMatrix  S4       
## df           82    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       82    -none-     numeric  
## dev.ratio    82    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames       10    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     1    -none-     logical  
## [1] "min lambda > lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##     2098.61180122       27.61931367      -39.63407436       -0.09460833 
## Date.last16.log1p  Date.last2.log1p Date.last32.log1p  Date.last4.log1p 
##      -89.45797902       -0.82554594      -41.58659723       -4.03296203 
##  Date.last8.log1p 
##       13.33757292 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##     2095.87460765       27.73800452      -39.52922603       -0.09483527 
## Date.last16.log1p  Date.last2.log1p Date.last32.log1p  Date.last4.log1p 
##      -89.97321520       -0.85098328      -41.02827399       -4.04835290 
##  Date.last8.log1p 
##       13.49586361 
## [1] "myfit_mdl: train diagnostics complete: 3.734000 secs"
## [1] "myfit_mdl: predict complete: 3.889000 secs"
##                               id
## 1 Max.cor.Y.Time.Lag##rcv#glmnet
##                                                                                                   feats
## 1 .pos,.inp.fctr,Date.last2.log1p,Date.last4.log1p,Date.last8.log1p,Date.last16.log1p,Date.last32.log1p
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.363                 0.008
##   max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1    0.4456674     41.81473        0.4433471    -16.20049     232.3414
##   max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1        -16.27249        0.4436578       1.961742          0.0210011
## [1] "myfit_mdl: exit: 3.898000 secs"
if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 3  fit.models_0_Max.cor.Y.Time.Lag          1          2      glmnet
## 4 fit.models_0_Interact.High.cor.Y          1          3      glmnet
##       bgn     end elapsed
## 3 123.610 127.523   3.913
## 4 127.524      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: .pos,.inp.fctr,.pos:.pos,.pos:Date.last2.log1p,.pos:.inp.fctr,.pos:Date.month.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.031 on full training set
## [1] "myfit_mdl: train complete: 2.742000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           100   -none-     numeric  
## beta        2100   dgCMatrix  S4       
## df           100   -none-     numeric  
## dim            2   -none-     numeric  
## lambda       100   -none-     numeric  
## dev.ratio    100   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        21   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      1   -none-     logical  
## [1] "min lambda > lambdaOpt:"
##            (Intercept)            .inp.fctrCC            .inp.fctrGE 
##          -9.762345e+00           1.832662e+02           9.656596e+01 
##           .inp.fctrIBM            .inp.fctrPG                   .pos 
##           2.542077e+02           1.121132e+02           2.636437e-02 
##       .inp.fctrCC:.pos      .inp.fctrIBM:.pos       .inp.fctrPG:.pos 
##          -9.382200e-02          -3.781724e-01          -4.684007e-02 
##  .pos:Date.last2.log1p .pos:Date.month.fctr02 .pos:Date.month.fctr03 
##          -4.366286e-03           4.024177e-04           4.235355e-04 
## .pos:Date.month.fctr04 .pos:Date.month.fctr05 .pos:Date.month.fctr06 
##           8.023870e-04           6.666666e-04          -6.779110e-05 
## .pos:Date.month.fctr07 .pos:Date.month.fctr08 .pos:Date.month.fctr09 
##          -6.328364e-04          -4.887492e-04          -8.643801e-04 
## .pos:Date.month.fctr10 .pos:Date.month.fctr11 .pos:Date.month.fctr12 
##          -1.105606e-03          -5.414138e-04          -5.782762e-06 
## [1] "max lambda < lambdaOpt:"
##            (Intercept)            .inp.fctrCC            .inp.fctrGE 
##          -1.781511e+01           1.928982e+02           1.048194e+02 
##           .inp.fctrIBM            .inp.fctrPG                   .pos 
##           2.623056e+02           1.208802e+02           3.011096e-02 
##       .inp.fctrCC:.pos      .inp.fctrIBM:.pos       .inp.fctrPG:.pos 
##          -9.847602e-02          -3.780618e-01          -5.114845e-02 
##  .pos:Date.last2.log1p .pos:Date.month.fctr02 .pos:Date.month.fctr03 
##          -4.636175e-03           3.809974e-04           3.999406e-04 
## .pos:Date.month.fctr04 .pos:Date.month.fctr05 .pos:Date.month.fctr06 
##           7.798091e-04           6.429269e-04          -1.026471e-04 
## .pos:Date.month.fctr07 .pos:Date.month.fctr08 .pos:Date.month.fctr09 
##          -6.688349e-04          -5.247464e-04          -9.009481e-04 
## .pos:Date.month.fctr10 .pos:Date.month.fctr11 .pos:Date.month.fctr12 
##          -1.143313e-03          -5.790881e-04          -4.460437e-05 
## [1] "myfit_mdl: train diagnostics complete: 3.378000 secs"
## [1] "myfit_mdl: predict complete: 3.531000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                feats
## 1 .pos,.inp.fctr,.pos:.pos,.pos:Date.last2.log1p,.pos:.inp.fctr,.pos:Date.month.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.041                 0.015
##   max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1    0.6172344     34.88221        0.6138542    -45.45308     381.8241
##   max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1         -45.8633         0.612902       1.459778         0.03746229
## [1] "myfit_mdl: exit: 3.541000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 4 fit.models_0_Interact.High.cor.Y          1          3      glmnet
## 5           fit.models_0_Low.cor.X          1          4      glmnet
##       bgn     end elapsed
## 4 127.524 131.075   3.551
## 5 131.075      NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames"
## [1] "myfit_mdl: setup complete: 0.707000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.144 on full training set
## [1] "myfit_mdl: train complete: 3.071000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            86   -none-     numeric  
## beta        2752   dgCMatrix  S4       
## df            86   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        86   -none-     numeric  
## dev.ratio     86   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        32   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      1   -none-     logical  
## [1] "min lambda > lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.798626e+03      2.238909e+01     -4.421574e+01     -7.255471e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.052143e-01     -1.215360e-02     -6.392147e-03     -1.013606e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.002126e+00     -3.358519e+00      3.029397e+00     -6.075795e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.717577e+00     -6.067895e-01     -1.042922e+00     -4.149733e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.120237e+00      6.543330e-01      1.070070e+00      3.014252e-01 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.841415e+03      2.294225e+01     -4.373128e+01     -7.327536e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.163244e-01     -1.248840e-02     -6.428555e-03     -1.039757e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.069648e+00     -3.430272e+00      3.092951e+00     -7.588476e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.741870e+00     -6.652235e-01     -1.102323e+00     -4.689666e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.172213e+00      6.976209e-01      1.113995e+00      3.503962e-01 
## [1] "myfit_mdl: train diagnostics complete: 3.686000 secs"
## [1] "myfit_mdl: predict complete: 3.858000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                      feats
## 1 .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.351                 0.012
##   max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1        0.446     42.07752        0.4385103    -3.099567     113.4293
##   max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1         -3.15499        0.4367487       2.060214         0.01913324
## [1] "myfit_mdl: exit: 3.867000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 5 fit.models_0_Low.cor.X          1          4      glmnet 131.075 134.961
## 6       fit.models_0_end          1          5    teardown 134.962      NA
##   elapsed
## 5   3.886
## 6      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          8          0           0 117.848 134.981  17.133
## 17 fit.models          8          1           1 134.981      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 136.462  NA      NA
# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indepVar <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
            indepVar <- setdiff(indepVar, topindep_var)
            if (length(interact_vars) > 0) {
                indepVar <- 
                    setdiff(indepVar, myextract_actual_feats(interact_vars))
                indepVar <- c(indepVar, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indepVar <- union(indepVar, topindep_var)
        }
    }
    
    if (is.null(indepVar))
        indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
        indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indepVar))
        indepVar <- mygetIndepVar(glb_feats_df)
    
    if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {    
        indepVar <- 
            eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
    }    

    indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indepVar <- setdiff(indepVar, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indepVar = indepVar, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indepVar]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 136.462 136.473
## 2 fit.models_1_All.X          1          1       setup 136.474      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 136.474 136.481
## 3 fit.models_1_All.X          1          2      glmnet 136.481      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.144 on full training set
## [1] "myfit_mdl: train complete: 2.755000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            86   -none-     numeric  
## beta        2752   dgCMatrix  S4       
## df            86   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        86   -none-     numeric  
## dev.ratio     86   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        32   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      1   -none-     logical  
## [1] "min lambda > lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.798626e+03      2.238909e+01     -4.421574e+01     -7.255471e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.052143e-01     -1.215360e-02     -6.392147e-03     -1.013606e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.002126e+00     -3.358519e+00      3.029397e+00     -6.075795e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.717577e+00     -6.067895e-01     -1.042922e+00     -4.149733e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.120237e+00      6.543330e-01      1.070070e+00      3.014252e-01 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.841415e+03      2.294225e+01     -4.373128e+01     -7.327536e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.163244e-01     -1.248840e-02     -6.428555e-03     -1.039757e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.069648e+00     -3.430272e+00      3.092951e+00     -7.588476e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.741870e+00     -6.652235e-01     -1.102323e+00     -4.689666e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.172213e+00      6.976209e-01      1.113995e+00      3.503962e-01 
## [1] "myfit_mdl: train diagnostics complete: 3.459000 secs"
## [1] "myfit_mdl: predict complete: 3.633000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                      feats
## 1 .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      2.045                 0.012
##   max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1        0.446     42.07752        0.4385103    -3.099567     113.4293
##   max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1         -3.15499        0.4367487       2.060214         0.01913324
## [1] "myfit_mdl: exit: 3.643000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 136.481 140.129
## 4 fit.models_1_All.X          1          3         glm 140.130      NA
##   elapsed
## 3   3.648
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indepVar: .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames"
## [1] "myfit_mdl: setup complete: 0.710000 secs"
## + Fold1.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.118000 secs"

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -111.354   -21.650    -3.038    12.678   273.399  
## 
## Coefficients: (3 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        6.757e+03  4.371e+03   1.546   0.1223    
## .inp.fctrCC       -4.145e+02  1.241e+03  -0.334   0.7384    
## .inp.fctrGE        3.584e+02  1.269e+03   0.282   0.7777    
## .inp.fctrIBM       3.970e+02  1.269e+03   0.313   0.7544    
## .inp.fctrPG       -4.432e+02  1.241e+03  -0.357   0.7210    
## .pos              -9.681e-02  6.185e-03 -15.653   <2e-16 ***
## .pos.y                    NA         NA      NA       NA    
## .rnorm             6.231e-01  8.369e-01   0.744   0.4567    
## .rownames                 NA         NA      NA       NA    
## Date.juliandate    3.554e+00  2.358e+00   1.507   0.1319    
## Date.last16.log1p -6.483e+02  2.981e+02  -2.175   0.0297 *  
## Date.last2.log1p  -6.160e+01  6.990e+01  -0.881   0.3783    
## Date.last32.log1p  1.528e+02  2.112e+02   0.723   0.4695    
## Date.last4.log1p   2.580e+01  8.397e+01   0.307   0.7587    
## Date.last8.log1p   7.455e+01  2.147e+02   0.347   0.7285    
## Date.month.fctr02 -9.968e+01  7.235e+01  -1.378   0.1684    
## Date.month.fctr03 -2.192e+02  1.447e+02  -1.515   0.1299    
## Date.month.fctr04 -3.359e+02  2.151e+02  -1.561   0.1186    
## Date.month.fctr05 -4.207e+02  2.852e+02  -1.475   0.1404    
## Date.month.fctr06 -5.396e+02  3.569e+02  -1.512   0.1307    
## Date.month.fctr07 -6.440e+02  4.283e+02  -1.504   0.1328    
## Date.month.fctr08 -7.572e+02  5.004e+02  -1.513   0.1304    
## Date.month.fctr09 -8.579e+02  5.725e+02  -1.498   0.1341    
## Date.month.fctr10 -9.726e+02  6.449e+02  -1.508   0.1317    
## Date.month.fctr11 -1.084e+03  7.172e+02  -1.512   0.1307    
## Date.month.fctr12 -1.186e+03  7.889e+02  -1.503   0.1330    
## Date.wkday.fctr1   1.525e-01  3.210e+00   0.048   0.9621    
## Date.wkday.fctr2   3.328e-01  3.199e+00   0.104   0.9172    
## Date.wkday.fctr3   3.642e-01  3.208e+00   0.114   0.9096    
## Date.wkday.fctr4   1.856e+00  3.188e+00   0.582   0.5605    
## Date.wkday.fctr5   6.916e-01  3.220e+00   0.215   0.8299    
## Date.wkday.fctr6   1.276e+00  3.201e+00   0.399   0.6902    
## Date.wkend                NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1752.843)
## 
##     Null deviance: 7532229  on 2399  degrees of freedom
## Residual deviance: 4154237  on 2370  degrees of freedom
## AIC: 24768
## 
## Number of Fisher Scoring iterations: 2
## 
## [1] "myfit_mdl: train diagnostics complete: 3.104000 secs"
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## [1] "myfit_mdl: predict complete: 3.234000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                      feats
## 1 .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                      1.395                 0.034
##   max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1    0.4484718     42.20301     24768.3        0.4417231    -188.8953
##   min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1     771.9928        -191.2189        0.4337656       2.014089
##   max.RsquaredSD.fit
## 1         0.01975136
## [1] "myfit_mdl: exit: 3.243000 secs"
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 140.130 143.383
## 5 fit.models_1_preProc          1          4     preProc 143.384      NA
##   elapsed
## 4   3.253
## 5      NA
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indepVar=indepVar, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indepVar
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indepVar_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indepVar_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indepVar_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indepVar=indepVar,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                                              id
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Max.cor.Y.Time.Lag##rcv#glmnet   Max.cor.Y.Time.Lag##rcv#glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                    feats
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                .pos,.inp.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                      .pos,.inp.fctr
## Max.cor.Y.Time.Lag##rcv#glmnet                                                                                     .pos,.inp.fctr,Date.last2.log1p,Date.last4.log1p,Date.last8.log1p,Date.last16.log1p,Date.last32.log1p
## Interact.High.cor.Y##rcv#glmnet                                                                                                       .pos,.inp.fctr,.pos:.pos,.pos:Date.last2.log1p,.pos:.inp.fctr,.pos:Date.month.fctr
## Low.cor.X##rcv#glmnet           .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
## All.X##rcv#glmnet               .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
## All.X##rcv#glm                  .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
##                                 max.nTuningRuns min.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.656
## Max.cor.Y##rcv#rpart                          5                      1.646
## Max.cor.Y.Time.Lag##rcv#glmnet               25                      2.363
## Interact.High.cor.Y##rcv#glmnet              25                      2.041
## Low.cor.X##rcv#glmnet                        25                      2.351
## All.X##rcv#glmnet                            25                      2.045
## All.X##rcv#glm                                1                      1.395
##                                 min.elapsedtime.final max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet                      0.011    0.4354461
## Max.cor.Y##rcv#rpart                            0.020    0.8113283
## Max.cor.Y.Time.Lag##rcv#glmnet                  0.008    0.4456674
## Interact.High.cor.Y##rcv#glmnet                 0.015    0.6172344
## Low.cor.X##rcv#glmnet                           0.012    0.4460000
## All.X##rcv#glmnet                               0.012    0.4460000
## All.X##rcv#glm                                  0.034    0.4484718
##                                 min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet          42.09290        0.4342670   -5.8717963
## Max.cor.Y##rcv#rpart                24.29990               NA   -0.2678863
## Max.cor.Y.Time.Lag##rcv#glmnet      41.81473        0.4433471  -16.2004933
## Interact.High.cor.Y##rcv#glmnet     34.88221        0.6138542  -45.4530763
## Low.cor.X##rcv#glmnet               42.07752        0.4385103   -3.0995666
## All.X##rcv#glmnet                   42.07752        0.4385103   -3.0995666
## All.X##rcv#glm                      42.20301        0.4417231 -188.8953069
##                                 min.RMSE.OOB max.Adj.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet         146.85585        -5.886148
## Max.cor.Y##rcv#rpart                63.08067               NA
## Max.cor.Y.Time.Lag##rcv#glmnet     232.34139       -16.272492
## Interact.High.cor.Y##rcv#glmnet    381.82413       -45.863301
## Low.cor.X##rcv#glmnet              113.42926        -3.154990
## All.X##rcv#glmnet                  113.42926        -3.154990
## All.X##rcv#glm                     771.99279      -191.218920
##                                 max.Rsquared.fit min.RMSESD.fit
## Max.cor.Y.rcv.1X1###glmnet                    NA             NA
## Max.cor.Y##rcv#rpart                   0.8083695       1.593100
## Max.cor.Y.Time.Lag##rcv#glmnet         0.4436578       1.961742
## Interact.High.cor.Y##rcv#glmnet        0.6129020       1.459778
## Low.cor.X##rcv#glmnet                  0.4367487       2.060214
## All.X##rcv#glmnet                      0.4367487       2.060214
## All.X##rcv#glm                         0.4337656       2.014089
##                                 max.RsquaredSD.fit min.aic.fit
## Max.cor.Y.rcv.1X1###glmnet                      NA          NA
## Max.cor.Y##rcv#rpart                    0.03917691          NA
## Max.cor.Y.Time.Lag##rcv#glmnet          0.02100110          NA
## Interact.High.cor.Y##rcv#glmnet         0.03746229          NA
## Low.cor.X##rcv#glmnet                   0.01913324          NA
## All.X##rcv#glmnet                       0.01913324          NA
## All.X##rcv#glm                          0.01975136     24768.3
rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 143.384 143.446
## 6     fit.models_1_end          1          5    teardown 143.447      NA
##   elapsed
## 5   0.062
## 6      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 17 fit.models          8          1           1 134.981 143.455   8.474
## 18 fit.models          8          2           2 143.456      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 145.093  NA      NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                                              id
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Max.cor.Y.Time.Lag##rcv#glmnet   Max.cor.Y.Time.Lag##rcv#glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                    feats
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                .pos,.inp.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                      .pos,.inp.fctr
## Max.cor.Y.Time.Lag##rcv#glmnet                                                                                     .pos,.inp.fctr,Date.last2.log1p,Date.last4.log1p,Date.last8.log1p,Date.last16.log1p,Date.last32.log1p
## Interact.High.cor.Y##rcv#glmnet                                                                                                       .pos,.inp.fctr,.pos:.pos,.pos:Date.last2.log1p,.pos:.inp.fctr,.pos:Date.month.fctr
## Low.cor.X##rcv#glmnet           .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
## All.X##rcv#glmnet               .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
## All.X##rcv#glm                  .inp.fctr,Date.last2.log1p,Date.last32.log1p,Date.last4.log1p,Date.wkday.fctr,Date.wkend,.rnorm,Date.last8.log1p,Date.last16.log1p,Date.juliandate,Date.month.fctr,.pos,.pos.y,.rownames
##                                 max.nTuningRuns max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet                    0    0.4354461
## Max.cor.Y##rcv#rpart                          5    0.8113283
## Max.cor.Y.Time.Lag##rcv#glmnet               25    0.4456674
## Interact.High.cor.Y##rcv#glmnet              25    0.6172344
## Low.cor.X##rcv#glmnet                        25    0.4460000
## All.X##rcv#glmnet                            25    0.4460000
## All.X##rcv#glm                                1    0.4484718
##                                 max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet             0.4342670   -5.8717963
## Max.cor.Y##rcv#rpart                          NA   -0.2678863
## Max.cor.Y.Time.Lag##rcv#glmnet         0.4433471  -16.2004933
## Interact.High.cor.Y##rcv#glmnet        0.6138542  -45.4530763
## Low.cor.X##rcv#glmnet                  0.4385103   -3.0995666
## All.X##rcv#glmnet                      0.4385103   -3.0995666
## All.X##rcv#glm                         0.4417231 -188.8953069
##                                 max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet             -5.886148               NA
## Max.cor.Y##rcv#rpart                          NA        0.8083695
## Max.cor.Y.Time.Lag##rcv#glmnet        -16.272492        0.4436578
## Interact.High.cor.Y##rcv#glmnet       -45.863301        0.6129020
## Low.cor.X##rcv#glmnet                  -3.154990        0.4367487
## All.X##rcv#glmnet                      -3.154990        0.4367487
## All.X##rcv#glm                       -191.218920        0.4337656
##                                 inv.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet                       1.5243902
## Max.cor.Y##rcv#rpart                             0.6075334
## Max.cor.Y.Time.Lag##rcv#glmnet                   0.4231909
## Interact.High.cor.Y##rcv#glmnet                  0.4899559
## Low.cor.X##rcv#glmnet                            0.4253509
## All.X##rcv#glmnet                                0.4889976
## All.X##rcv#glm                                   0.7168459
##                                 inv.elapsedtime.final inv.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet                   90.90909   0.02375698
## Max.cor.Y##rcv#rpart                         50.00000   0.04115242
## Max.cor.Y.Time.Lag##rcv#glmnet              125.00000   0.02391502
## Interact.High.cor.Y##rcv#glmnet              66.66667   0.02866791
## Low.cor.X##rcv#glmnet                        83.33333   0.02376566
## All.X##rcv#glmnet                            83.33333   0.02376566
## All.X##rcv#glm                               29.41176   0.02369499
##                                 inv.RMSE.OOB  inv.aic.fit
## Max.cor.Y.rcv.1X1###glmnet       0.006809398           NA
## Max.cor.Y##rcv#rpart             0.015852717           NA
## Max.cor.Y.Time.Lag##rcv#glmnet   0.004304011           NA
## Interact.High.cor.Y##rcv#glmnet  0.002619007           NA
## Low.cor.X##rcv#glmnet            0.008816067           NA
## All.X##rcv#glmnet                0.008816067           NA
## All.X##rcv#glm                   0.001295349 4.037419e-05
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 1 rows containing missing values (position_stack).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                                id min.RMSE.OOB max.R.sq.OOB
## 2            Max.cor.Y##rcv#rpart     63.08067   -0.2678863
## 5           Low.cor.X##rcv#glmnet    113.42926   -3.0995666
## 6               All.X##rcv#glmnet    113.42926   -3.0995666
## 1      Max.cor.Y.rcv.1X1###glmnet    146.85585   -5.8717963
## 3  Max.cor.Y.Time.Lag##rcv#glmnet    232.34139  -16.2004933
## 4 Interact.High.cor.Y##rcv#glmnet    381.82413  -45.4530763
## 7                  All.X##rcv#glm    771.99279 -188.8953069
##   max.Adj.R.sq.fit min.RMSE.fit
## 2               NA     24.29990
## 5        0.4385103     42.07752
## 6        0.4385103     42.07752
## 1        0.4342670     42.09290
## 3        0.4433471     41.81473
## 4        0.6138542     34.88221
## 7        0.4417231     42.20301
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fit + min.RMSE.fit
## <environment: 0x7fa5ecee0260>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: Max.cor.Y##rcv#rpart"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glb_sel_mdl_id)) 
    glb_sel_mdl_id <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glb_sel_mdl_id))   
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])

##             Length Class      Mode     
## a0            86   -none-     numeric  
## beta        2752   dgCMatrix  S4       
## df            86   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        86   -none-     numeric  
## dev.ratio     86   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        32   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      1   -none-     logical  
## [1] "min lambda > lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.798626e+03      2.238909e+01     -4.421574e+01     -7.255471e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.052143e-01     -1.215360e-02     -6.392147e-03     -1.013606e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.002126e+00     -3.358519e+00      3.029397e+00     -6.075795e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.717577e+00     -6.067895e-01     -1.042922e+00     -4.149733e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.120237e+00      6.543330e-01      1.070070e+00      3.014252e-01 
## [1] "max lambda < lambdaOpt:"
##       (Intercept)       .inp.fctrCC       .inp.fctrGE              .pos 
##      1.841415e+03      2.294225e+01     -4.373128e+01     -7.327536e-02 
##            .rnorm         .rownames   Date.juliandate Date.last16.log1p 
##      5.163244e-01     -1.248840e-02     -6.428555e-03     -1.039757e+02 
##  Date.last2.log1p  Date.last4.log1p Date.month.fctr02 Date.month.fctr03 
##     -1.069648e+00     -3.430272e+00      3.092951e+00     -7.588476e-01 
## Date.month.fctr05 Date.month.fctr06 Date.month.fctr07 Date.month.fctr08 
##      1.741870e+00     -6.652235e-01     -1.102323e+00     -4.689666e-01 
## Date.month.fctr10 Date.month.fctr12  Date.wkday.fctr4  Date.wkday.fctr6 
##     -1.172213e+00      6.976209e-01      1.113995e+00      3.503962e-01
## [1] TRUE
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##                   All.X..rcv.glmnet.imp          imp
## Date.last16.log1p          1.000000e+02 1.000000e+02
## .inp.fctrGE                4.273554e+01 4.273554e+01
## .inp.fctrCC                2.207520e+01 2.207520e+01
## Date.last4.log1p           3.305309e+00 3.305309e+00
## Date.month.fctr02          2.980765e+00 2.980765e+00
## Date.month.fctr05          1.683599e+00 1.683599e+00
## Date.month.fctr10          1.117788e+00 1.117788e+00
## Date.wkday.fctr4           1.064609e+00 1.064609e+00
## Date.month.fctr07          1.046650e+00 1.046650e+00
## Date.last2.log1p           1.011407e+00 1.011407e+00
## Date.month.fctr03          6.733998e-01 6.733998e-01
## Date.month.fctr12          6.599563e-01 6.599563e-01
## Date.month.fctr06          6.219835e-01 6.219835e-01
## .rnorm                     4.973828e-01 4.973828e-01
## Date.month.fctr08          4.330194e-01 4.330194e-01
## Date.wkday.fctr6           3.198537e-01 3.198537e-01
## .pos                       7.095269e-02 7.095269e-02
## .rownames                  1.200204e-02 1.200204e-02
## Date.juliandate            6.236232e-03 6.236232e-03
## .inp.fctrIBM               0.000000e+00 0.000000e+00
## .inp.fctrPG                0.000000e+00 0.000000e+00
## .pos.y                     0.000000e+00 0.000000e+00
## Date.last32.log1p          0.000000e+00 0.000000e+00
## Date.last8.log1p           0.000000e+00 0.000000e+00
## Date.month.fctr04          0.000000e+00 0.000000e+00
## Date.month.fctr09          0.000000e+00 0.000000e+00
## Date.month.fctr11          0.000000e+00 0.000000e+00
## Date.wkday.fctr1           0.000000e+00 0.000000e+00
## Date.wkday.fctr2           0.000000e+00 0.000000e+00
## Date.wkday.fctr3           0.000000e+00 0.000000e+00
## Date.wkday.fctr5           0.000000e+00 0.000000e+00
## Date.wkend                 0.000000e+00 0.000000e+00
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id, 
            prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 14

##        Date StockPrice .inp .src     .rnorm .pos .pos.y .rownames
## 2438 2/1/73   438.9016  IBM Test -0.4353803 2438   2438      2438
## 2439 3/1/73   437.4041  IBM Test  0.2302633 2439   2439      2439
## 2437 1/1/73   424.5062  IBM Test -1.0371386 2437   2437      2437
## 2440 4/1/73   423.7250  IBM Test -1.2322230 2440   2440      2440
## 2432 8/1/72   413.7174  IBM Test  0.9813313 2432   2432      2432
##      Date.POSIX Date.year.fctr Date.month.fctr  Date.date.fctr
## 2438 1973-02-01           1973              02 (0.9998,1.0002]
## 2439 1973-03-01           1973              03 (0.9998,1.0002]
## 2437 1973-01-01           1973              01 (0.9998,1.0002]
## 2440 1973-04-01           1973              04 (0.9998,1.0002]
## 2432 1972-08-01           1972              08 (0.9998,1.0002]
##      Date.juliandate Date.wkday.fctr Date.wkend Date.hlday Date.hour.fctr
## 2438              32               4          0          0              0
## 2439              60               4          0          0              0
## 2437               1               1          0          0              0
## 2440              91               0          1          0              0
## 2432             214               2          0          0              0
##      Date.minute.fctr Date.second.fctr Date.day.minutes  Date.zoo
## 2438                0                0                0 631170000
## 2439                0                0                0 631170000
## 2437                0                0                0 631170000
## 2440                0                0                0 631170000
## 2432                0                0                0 631170000
##      Date.last2.log1p Date.last4.log1p Date.last8.log1p Date.last16.log1p
## 2438                0                0         14.80073          15.49388
## 2439                0                0         14.69895          15.44428
## 2437                0                0         14.80073          15.47762
## 2440                0                0         14.80073          15.44428
## 2432                0                0         14.80073          15.47762
##      Date.last32.log1p .inp.fctr StockPrice.All.X..rcv.glmnet
## 2438          15.88853       IBM                     25.20100
## 2439          15.86655       IBM                     26.61903
## 2437          15.88898       IBM                     22.68959
## 2440          15.86655       IBM                     25.18468
## 2432          15.88853       IBM                     22.33729
##      StockPrice.All.X..rcv.glmnet.err StockPrice.All.X..rcv.glmnet.err.abs
## 2438                         413.7006                             413.7006
## 2439                         410.7851                             410.7851
## 2437                         401.8166                             401.8166
## 2440                         398.5403                             398.5403
## 2432                         391.3801                             391.3801
##      StockPrice.All.X..rcv.glmnet.is.acc .label
## 2438                               FALSE   2438
## 2439                               FALSE   2439
## 2437                               FALSE   2437
## 2440                               FALSE   2440
## 2432                               FALSE   2432

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##     .inp .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## IBM  IBM    480    480    480            0.2            0.2            0.2
## BG    BG    480    480    480            0.2            0.2            0.2
## CC    CC    480    480    480            0.2            0.2            0.2
## PG    PG    480    480    480            0.2            0.2            0.2
## GE    GE    480    480    480            0.2            0.2            0.2
##     err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## IBM       29340.236         61.12549    480        66716.72
## BG        10452.639         21.77633    480        64174.95
## CC         8013.138         16.69404    480        40085.31
## PG         7099.550         14.79073    480        39804.47
## GE         8784.733         18.30153    480        32424.37
##     err.abs.OOB.mean
## IBM        138.99317
## BG         133.69781
## CC          83.51107
## PG          82.92599
## GE          67.55078
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##        2400.0000        2400.0000        2400.0000           1.0000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##           1.0000           1.0000       63690.2954         132.6881 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##        2400.0000      243205.8281         506.6788
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glb_sel_mdl_id), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 154.723  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 18 fit.models          8          2           2 143.456 154.733  11.277
## 19 fit.models          8          3           3 154.733      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn=glb_analytics_pn, 
    replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord=TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn    end
## 19        fit.models          8          3           3 154.733 161.69
## 20 fit.data.training          9          0           0 161.690     NA
##    elapsed
## 19   6.957
## 20      NA

Step 9.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glb_sel_mdl_id")
    glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indepVar <- mygetIndepVar(glb_feats_df)
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
        
    if (grepl("Ensemble", glb_sel_mdl_id)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
        indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glb_sel_mdl_id
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glb_sel_mdl_id)) != -1))
        ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}
## Warning: Final model same as glb_sel_mdl_id
rm(ret_lst)
## Warning in rm(ret_lst): object 'ret_lst' not found
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 20 fit.data.training          9          0           0 161.690 162.136
## 21 fit.data.training          9          1           1 162.137      NA
##    elapsed
## 20   0.446
## 21      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glb_fin_mdl_id)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glb_fin_mdl_id)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)

glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                   All.X..rcv.glmnet.imp          imp
## Date.last16.log1p          1.000000e+02 1.000000e+02
## .inp.fctrGE                4.273554e+01 4.273554e+01
## .inp.fctrCC                2.207520e+01 2.207520e+01
## Date.last4.log1p           3.305309e+00 3.305309e+00
## Date.month.fctr02          2.980765e+00 2.980765e+00
## Date.month.fctr05          1.683599e+00 1.683599e+00
## Date.month.fctr10          1.117788e+00 1.117788e+00
## Date.wkday.fctr4           1.064609e+00 1.064609e+00
## Date.month.fctr07          1.046650e+00 1.046650e+00
## Date.last2.log1p           1.011407e+00 1.011407e+00
## Date.month.fctr03          6.733998e-01 6.733998e-01
## Date.month.fctr12          6.599563e-01 6.599563e-01
## Date.month.fctr06          6.219835e-01 6.219835e-01
## .rnorm                     4.973828e-01 4.973828e-01
## Date.month.fctr08          4.330194e-01 4.330194e-01
## Date.wkday.fctr6           3.198537e-01 3.198537e-01
## .pos                       7.095269e-02 7.095269e-02
## .rownames                  1.200204e-02 1.200204e-02
## Date.juliandate            6.236232e-03 6.236232e-03
## .inp.fctrIBM               0.000000e+00 0.000000e+00
## .inp.fctrPG                0.000000e+00 0.000000e+00
## .pos.y                     0.000000e+00 0.000000e+00
## Date.last32.log1p          0.000000e+00 0.000000e+00
## Date.last8.log1p           0.000000e+00 0.000000e+00
## Date.month.fctr04          0.000000e+00 0.000000e+00
## Date.month.fctr09          0.000000e+00 0.000000e+00
## Date.month.fctr11          0.000000e+00 0.000000e+00
## Date.wkday.fctr1           0.000000e+00 0.000000e+00
## Date.wkday.fctr2           0.000000e+00 0.000000e+00
## Date.wkday.fctr3           0.000000e+00 0.000000e+00
## Date.wkday.fctr5           0.000000e+00 0.000000e+00
## Date.wkend                 0.000000e+00 0.000000e+00
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id, 
            prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 14

##      Date StockPrice .inp  .src      .rnorm .pos .pos.y .rownames
## 38 2/1/73   438.9016  IBM Train -1.75677234   38     38        38
## 39 3/1/73   437.4041  IBM Train -0.38434913   39     39        39
## 37 1/1/73   424.5062  IBM Train  0.41352017   37     37        37
## 40 4/1/73   423.7250  IBM Train  0.03167139   40     40        40
## 32 8/1/72   413.7174  IBM Train -1.66333330   32     32        32
##    Date.POSIX Date.year.fctr Date.month.fctr Date.juliandate
## 38 1973-02-01           1973              02              32
## 39 1973-03-01           1973              03              60
## 37 1973-01-01           1973              01               1
## 40 1973-04-01           1973              04              91
## 32 1972-08-01           1972              08             214
##    Date.wkday.fctr Date.wkend Date.zoo Date.last2.log1p Date.last4.log1p
## 38               4          0    18000         14.80073         14.80073
## 39               4          0    18000         14.69895         14.69895
## 37               1          0    18000         14.80073         14.80073
## 40               0          1    18000         14.80073         14.80073
## 32               2          0    18000         14.80073         14.80073
##    Date.last8.log1p Date.last16.log1p Date.last32.log1p .inp.fctr .lcn
## 38         14.80073          15.49388          16.17927       IBM  Fit
## 39         14.69895          15.44428          16.15423       IBM  Fit
## 37         14.80073          15.47762          16.17111       IBM  Fit
## 40         14.80073          15.44428          16.16253       IBM  Fit
## 32         14.80073          15.47762          16.17042       IBM  Fit
##     Date.date.fctr Date.hlday Date.hour.fctr Date.minute.fctr
## 38 (0.9998,1.0002]          0              0                0
## 39 (0.9998,1.0002]          0              0                0
## 37 (0.9998,1.0002]          0              0                0
## 40 (0.9998,1.0002]          0              0                0
## 32 (0.9998,1.0002]          0              0                0
##    Date.second.fctr Date.day.minutes StockPrice.All.X..rcv.glmnet
## 38                0                0                     163.5490
## 39                0                0                     165.7803
## 37                0                0                     162.4554
## 40                0                0                     164.8549
## 32                0                0                     160.0085
##    StockPrice.All.X..rcv.glmnet.err StockPrice.All.X..rcv.glmnet.err.abs
## 38                        -275.3526                             275.3526
## 39                        -271.6238                             271.6238
## 37                        -262.0508                             262.0508
## 40                        -258.8701                             258.8701
## 32                        -253.7089                             253.7089
##    StockPrice.All.X..rcv.glmnet.is.acc StockPrice.Final.All.X..rcv.glmnet
## 38                               FALSE                           163.5490
## 39                               FALSE                           165.7803
## 37                               FALSE                           162.4554
## 40                               FALSE                           164.8549
## 32                               FALSE                           160.0085
##    StockPrice.Final.All.X..rcv.glmnet.err
## 38                               275.3526
## 39                               271.6238
## 37                               262.0508
## 40                               258.8701
## 32                               253.7089
##    StockPrice.Final.All.X..rcv.glmnet.err.abs
## 38                                   275.3526
## 39                                   271.6238
## 37                                   262.0508
## 40                                   258.8701
## 32                                   253.7089
##    StockPrice.Final.All.X..rcv.glmnet.is.acc .label
## 38                                     FALSE     38
## 39                                     FALSE     39
## 37                                     FALSE     37
## 40                                     FALSE     40
## 32                                     FALSE     32

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "StockPrice.Final.All.X..rcv.glmnet"        
## [2] "StockPrice.Final.All.X..rcv.glmnet.err"    
## [3] "StockPrice.Final.All.X..rcv.glmnet.err.abs"
## [4] "StockPrice.Final.All.X..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 21 fit.data.training          9          1           1 162.137 169.958
## 22  predict.data.new         10          0           0 169.959      NA
##    elapsed
## 21   7.821
## 22      NA

Step 10.0: predict data new

## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 14

##        Date StockPrice .inp .src     .rnorm .pos .pos.y .rownames
## 2438 2/1/73   438.9016  IBM Test -0.4353803 2438   2438      2438
## 2439 3/1/73   437.4041  IBM Test  0.2302633 2439   2439      2439
## 2437 1/1/73   424.5062  IBM Test -1.0371386 2437   2437      2437
## 2440 4/1/73   423.7250  IBM Test -1.2322230 2440   2440      2440
## 2432 8/1/72   413.7174  IBM Test  0.9813313 2432   2432      2432
##      Date.POSIX Date.year.fctr Date.month.fctr Date.juliandate
## 2438 1973-02-01           1973              02              32
## 2439 1973-03-01           1973              03              60
## 2437 1973-01-01           1973              01               1
## 2440 1973-04-01           1973              04              91
## 2432 1972-08-01           1972              08             214
##      Date.wkday.fctr Date.wkend  Date.zoo Date.last2.log1p
## 2438               4          0 631170000                0
## 2439               4          0 631170000                0
## 2437               1          0 631170000                0
## 2440               0          1 631170000                0
## 2432               2          0 631170000                0
##      Date.last4.log1p Date.last8.log1p Date.last16.log1p Date.last32.log1p
## 2438                0         14.80073          15.49388          15.88853
## 2439                0         14.69895          15.44428          15.86655
## 2437                0         14.80073          15.47762          15.88898
## 2440                0         14.80073          15.44428          15.86655
## 2432                0         14.80073          15.47762          15.88853
##      .inp.fctr .lcn StockPrice.Final.All.X..rcv.glmnet
## 2438       IBM  OOB                           25.20100
## 2439       IBM  OOB                           26.61903
## 2437       IBM  OOB                           22.68959
## 2440       IBM  OOB                           25.18468
## 2432       IBM  OOB                           22.33729
##      StockPrice.Final.All.X..rcv.glmnet.err
## 2438                               413.7006
## 2439                               410.7851
## 2437                               401.8166
## 2440                               398.5403
## 2432                               391.3801
##      StockPrice.Final.All.X..rcv.glmnet.err.abs
## 2438                                   413.7006
## 2439                                   410.7851
## 2437                                   401.8166
## 2440                                   398.5403
## 2432                                   391.3801
##      StockPrice.Final.All.X..rcv.glmnet.is.acc .label
## 2438                                     FALSE   2438
## 2439                                     FALSE   2439
## 2437                                     FALSE   2437
## 2440                                     FALSE   2440
## 2432                                     FALSE   2432

## [1] TRUE
## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"
## [1] "glb_fin_mdl_id: Final.All.X##rcv#glmnet"
## [1] "Cross Validation issues:"
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                 min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## Max.cor.Y##rcv#rpart                63.08067   -0.2678863               NA
## Low.cor.X##rcv#glmnet              113.42926   -3.0995666        0.4385103
## All.X##rcv#glmnet                  113.42926   -3.0995666        0.4385103
## Max.cor.Y.rcv.1X1###glmnet         146.85585   -5.8717963        0.4342670
## Max.cor.Y.Time.Lag##rcv#glmnet     232.34139  -16.2004933        0.4433471
## Interact.High.cor.Y##rcv#glmnet    381.82413  -45.4530763        0.6138542
## All.X##rcv#glm                     771.99279 -188.8953069        0.4417231
##                                 min.RMSE.fit
## Max.cor.Y##rcv#rpart                24.29990
## Low.cor.X##rcv#glmnet               42.07752
## All.X##rcv#glmnet                   42.07752
## Max.cor.Y.rcv.1X1###glmnet          42.09290
## Max.cor.Y.Time.Lag##rcv#glmnet      41.81473
## Interact.High.cor.Y##rcv#glmnet     34.88221
## All.X##rcv#glm                      42.20301
## [1] "All.X##rcv#glmnet OOB RMSE: 113.4293"
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## IBM       29340.236        66716.72       29340.236        66716.72
## BG        10452.639        64174.95       10452.639        64174.95
## CC         8013.138        40085.31        8013.138        40085.31
## PG         7099.550        39804.47        7099.550        39804.47
## GE         8784.733        32424.37        8784.733        32424.37
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.OOB .n.Tst
## IBM            0.2            0.2            0.2    480    480    480
## BG             0.2            0.2            0.2    480    480    480
## CC             0.2            0.2            0.2    480    480    480
## PG             0.2            0.2            0.2    480    480    480
## GE             0.2            0.2            0.2    480    480    480
##     .n.fit .n.new .n.trn err.abs.OOB.mean err.abs.fit.mean
## IBM    480    480    480        138.99317         61.12549
## BG     480    480    480        133.69781         21.77633
## CC     480    480    480         83.51107         16.69404
## PG     480    480    480         82.92599         14.79073
## GE     480    480    480         67.55078         18.30153
##     err.abs.new.mean err.abs.trn.mean
## IBM        138.99317         61.12549
## BG         133.69781         21.77633
## CC          83.51107         16.69404
## PG          82.92599         14.79073
## GE          67.55078         18.30153
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##       63690.2954      243205.8281       63690.2954      243205.8281 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##           1.0000           1.0000           1.0000        2400.0000 
##           .n.OOB           .n.Tst           .n.fit           .n.new 
##        2400.0000        2400.0000        2400.0000        2400.0000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##        2400.0000         506.6788         132.6881         506.6788 
## err.abs.trn.mean 
##         132.6881
## [1] "Final.All.X##rcv#glmnet prediction stats for glbObsNew:"
##                  id max.R.sq.new min.RMSE.new max.Adj.R.sq.new
## 1 All.X##rcv#glmnet    -3.099567     113.4293         -3.15499
## [1] "Features Importance for selected models:"
##                   All.X..rcv.glmnet.imp
## Date.last16.log1p             100.00000
## .inp.fctrGE                    42.73554
## .inp.fctrCC                    22.07520
## [1] "glbObsNew prediction stats:"
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##                   label step_major step_minor label_minor     bgn     end
## 22     predict.data.new         10          0           0 169.959 188.543
## 23 display.session.info         11          0           0 188.544      NA
##    elapsed
## 22  18.584
## 23      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 2               inspect.data          2          0           0   8.854
## 6  extract.features.datetime          3          1           1  76.288
## 3                 scrub.data          2          1           1  48.691
## 22          predict.data.new         10          0           0 169.959
## 16                fit.models          8          0           0 117.848
## 18                fit.models          8          2           2 143.456
## 17                fit.models          8          1           1 134.981
## 21         fit.data.training          9          1           1 162.137
## 19                fit.models          8          3           3 154.733
## 7     extract.features.image          3          2           2 110.590
## 1                import.data          1          0           0   6.273
## 15           select.features          7          0           0 115.812
## 11      extract.features.end          3          6           6 114.247
## 20         fit.data.training          9          0           0 161.690
## 12       manage.missing.data          4          0           0 115.160
## 14   partition.data.training          6          0           0 115.529
## 10   extract.features.string          3          5           5 114.187
## 9      extract.features.text          3          4           4 114.133
## 13              cluster.data          5          0           0 115.476
## 4             transform.data          2          2           2  76.226
## 8     extract.features.price          3          3           3 114.094
## 5           extract.features          3          0           0  76.267
##        end elapsed duration
## 2   48.690  39.836   39.836
## 6  110.590  34.302   34.302
## 3   76.225  27.534   27.534
## 22 188.543  18.584   18.584
## 16 134.981  17.133   17.133
## 18 154.733  11.277   11.277
## 17 143.455   8.474    8.474
## 21 169.958   7.821    7.821
## 19 161.690   6.957    6.957
## 7  114.094   3.504    3.504
## 1    8.854   2.581    2.581
## 15 117.848   2.036    2.036
## 11 115.160   0.913    0.913
## 20 162.136   0.446    0.446
## 12 115.476   0.316    0.316
## 14 115.811   0.282    0.282
## 10 114.247   0.060    0.060
## 9  114.186   0.053    0.053
## 13 115.529   0.053    0.053
## 4   76.267   0.041    0.041
## 8  114.132   0.039    0.038
## 5   76.288   0.021    0.021
## [1] "Total Elapsed Time: 188.543 secs"